Application of Machine Learning for Gas Well Production Prediction in the Xushen Gas Field

Gas well production forecasting serves as a critical foundation for optimizing production systems and evaluating the development performance of gas reservoirs. Conventional decline curve analysis is primarily applicable to gas fields exhibiting rapid production decline. However, in the Xushen Gas Field, the production decline trend is moderate, and certain wells demonstrate significant production fluctuations. This study investigates the application of machine learning methods for gas well production forecasting in the Xushen Gas Field. Using production data from individual wells, a forecasting model based on the Long Short-Term Memory (LSTM) neural network is developed. The characteristics of production data are analyzed, and the data preprocessing workflow is established. The correlations between dynamic production parameters and gas output are examined. An LSTM model is then constructed using production days, gas–water ratio, and wellhead pressure as input features, and gas production as the output variable. The proposed method is applied to predict both daily gas production and the monthly average daily gas production for individual wells in the Xushen Gas Field. The mean relative error of prediction results remains within 15%, demonstrating that the forecasting accuracy meets field operational requirements.